Indoor positioning applications generally require high accuracy. Indoor positioning, however, is a challenging problem as global positioning system (GPS) signals are often not detectable in indoor environments. There exist a number of radio frequency (RF)-based indoor positioning methods that rely on the use of RF (wireless local area network (WLAN), Bluetooth®, etc.) for indoor positioning. These methods estimate the time-of-arrival (ToA) and/or angle-of-arrival (AoA) of RF signals that propagate between a mobile device and a set of fixed access points (e.g., WLAN access points with known a posteriori coordinates). The methods then use triangulation to calculate the position of the device.
These methods work very well when direct line-of-sight (LoS) exists between the mobile device and the access points. However, the performance of these methods degrades very rapidly in multipath environments, where large numbers of reflections are present in the environment and/or no direct LoS is accessible.
Some have suggested the use of pre-scanning (fingerprinting) of the environment's propagation profile as a function of location coordinates to be able to correlate and uniquely map a measured propagation profile to a position. Although this method is resilient to lack of LoS paths, it requires the pre-scanning phase, which may be impractical or costly in many usage applications.